analyze.viral_moments

Operation: analyze.viral_moments
Category: ai
Tags: ai, analysis, clips, viral

AI analysis of transcript + scene boundaries to identify viral clip candidates

Runtime

Type: AI Provider (routed via provider registry)
Timeout: 300s
Retries: 3 (ExponentialWithJitter)

Inputs

Name	Type	Required	Default	Description
transcript	String	Yes	—	Full transcript text (with timestamps if available)
scenes	JSON	No	—	Scene boundary data from scene_detect
duration	Number	No	—	Total video duration in seconds

Outputs

Name	Type	Description
viral_moments	JSON	Array of viral moment objects
count	Number	Number of moments identified

Default Configuration

{
  "max_clips": 10,
  "max_duration_secs": 90,
  "min_clips": 3,
  "min_duration_secs": 15,
  "modality": "text",
  "provider": "gemini"
}

Usage

TypeScript

import { WorkflowBuilder } from "@fabric-platform/sdk";

const workflow = new WorkflowBuilder("my-workflow")
  .node("analyze-viral-moments", "ai_invoke", (n) =>
    n.config({
      operation: "analyze.viral_moments",
      // ... node-specific config
    })
  )
  .build();

Python

from fabric_platform import FabricClient

fabric = FabricClient(api_key="fab_xxx")

wf_id = fabric.upsert_workflow("my-workflow", nodes=[
    {
        "key": "analyze-viral-moments",
        "kind": "ai_invoke",
    },
])

Rust

use fabric_sdk::FabricClient;

let client = FabricClient::new("http://localhost:3001", api_key)?;

let wf_id = client.upsert_workflow("my-workflow", serde_json::json!({
    "nodes": [{
        "key": "analyze-viral-moments",
        "kind": "ai_invoke"
    }]
})).await?;

curl

curl -X POST http://localhost:3001/v1/workflow-definitions \
  -H "Authorization: Bearer $FABRIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "my-workflow",
    "nodes": [{
      "key": "analyze-viral-moments",
      "kind": "ai_invoke"
    }]
  }'